Cost Effectiveness of Intracranial Pressure Monitoring in Pediatric Patients with Severe Traumatic Brain Injury: A Simulation Modeling Approach.

OBJECTIVES: To conduct an economic evaluation of intracranial pressure (ICP) monitoring on the basis of current evidence from pediatric patients with severe traumatic brain injury, through a statistical model. METHODS: The statistical model is a decision tree, whose branches take into account the severity of the lesion, the hospitalization costs, and the quality-adjusted life-year for the first 6 months post-trauma. The inputs consist of probability distributions calculated from a sample of 33 surviving children with severe traumatic brain injury, divided into two groups: with ICP monitoring (monitoring group) and without ICP monitoring (control group). The uncertainty of the parameters from the sample was quantified through a probabilistic sensitivity analysis using the Monte-Carlo simulation method. The model overcomes the drawbacks of small sample sizes, unequal groups, and the ethical difficulty in randomly assigning patients to a control group (without monitoring). RESULTS: The incremental cost in the monitoring group was Mex$3,934 (Mexican pesos), with an increase in quality-adjusted life-year of 0.05. The incremental cost-effectiveness ratio was Mex$81,062. The cost-effectiveness acceptability curve had a maximum at 54% of the cost effective iterations. The incremental net health benefit for a willingness to pay equal to 1 time the per capita gross domestic product for Mexico was 0.03, and the incremental net monetary benefit was Mex$5,358. CONCLUSIONS: The results of the model suggest that ICP monitoring is cost effective because there was a monetary gain in terms of the incremental net monetary benefit.

Discriminant function of perinatal risk that predicts early neonatal morbidity: its validity and reliability.

BACKGROUND: This study aimed to identify significant perinatal risk factors associated with neonatal morbidity to construct a scoring system to aid in distinguishing between healthy and ill neonates. Validity and reliability of the scoring system were determined. METHODS: We conducted a screening test and used logistic regression to analyze data from a cohort of 387 neonates and to determine the relationship between risk factors and morbidity. Twenty nine factors of perinatal risk were studied. Logistic regression and discriminant analysis were performed to assess risk for morbidity. This system was developed and validated prospectively on 238 new neonates. RESULTS: Risk factors that demonstrated association with morbidity by logistic regression were chronic maternal illness, premature rupture of membranes (PROM), amniotic fluid, low Apgar score at 5 min, obstetric trauma, hypertension, neonatal resuscitation, breathing pattern at 6 h after delivery, birth weight, and gestational age. Discriminant function obtained from discriminant analysis had sensitivity of 68% and specificity of 93%, while positive and negative predictive values were 88 and 86%, respectively. Area below receiver operating characteristic (ROC) curve was 0.86 (standard error [SE]: 0.02). In the validity study, these values were maintained without significant differences. Kappa statistic between two physicians was calculated at 0.84 (p < 0.001). CONCLUSIONS: Evidence indicated that discriminant function is a useful tool to assess initial neonatal risk, allowing pediatricians to predict morbidity prior to discharge of neonates.